Can it help with RAG implementation?

Yes, it provides specialized workflows for document chunking, embedding generation, and building complete Retrieval-Augmented Generation pipelines.

What embedding models are covered?

It includes implementation examples and best practices for popular providers like OpenAI and HuggingFace.

Is it suitable for production environments?

Yes, it focuses on production-grade concerns like index mappings, query scoring optimization, and efficient metadata filtering.

What search engines does this skill support?

It supports traditional full-text engines like Elasticsearch and OpenSearch, as well as vector-native databases like Pinecone, Chroma, and pgvector.

Does it support hybrid search?

Absolutely. It provides code patterns for combining keyword-based (BM25) and semantic vector search to maximize retrieval accuracy.

Search & Vector Architect

Name: Search & Vector Architect
Author: k1lgor

byk1lgor

0•

Data Science & ML

Architects and implements high-performance full-text search engines and vector retrieval systems for AI-driven applications.

The Search & Vector Architect skill empowers Claude to design and deploy sophisticated search infrastructures, ranging from traditional keyword-based systems like Elasticsearch to modern semantic vector databases like Pinecone and Chroma. It provides expert guidance on index mapping, query optimization, document chunking strategies for RAG pipelines, and hybrid search implementation. This skill is essential for developers building context-aware AI applications that require fast, accurate, and scalable data retrieval from massive datasets.

Key Features

01Full-text search optimization for Elasticsearch and OpenSearch

02Vector database integration with Pinecone, Chroma, and pgvector

030 GitHub stars

04Retrieval-Augmented Generation (RAG) pipeline design and chunking

05Hybrid search implementation combining BM25 and semantic similarity

06Advanced metadata filtering and custom analyzer configurations

Use Cases

01Building a production-grade RAG system for technical documentation

02Implementing semantic product search for e-commerce platforms

03Optimizing database performance for high-volume text and vector queries

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add k1lgor/virtual-company 23-search-vector-architect

For use in Claude.ai and ChatGPT

Download Skill